How can I resize a PyTorch tensor with a sliding window?

Question

I have a tensor with size: torch.Size([118160, 1]) . What I want to do is split it up into n tensors with 100 elements each, sliding by 50 elements at a time. What's the best way to achieve this with PyTorch?

Answer 1

Your number of elements needs to be divisble by 100. If this is not the case you can adjust with padding.

You can first do a split on the original list. Then do a split on the list where the first 50 elements are removed from the original list. You can then sample alternating order from A and B if you want to preserve original order.

A = yourtensor
B = yourtensor[50:] + torch.zeros(50,1)
A_ = A.view(100,-1)
B_ = B.view(100,-1)

Answer 2

A possible solution is:

window_size = 100
stride = 50
splits = [x[i:min(x.size(0),i+window_size)] for i in range(0,x.size(0),stride)]

However, the last few elements will be shorter than window_size . If this is undesired, you can do:

splits = [x[i:i+window_size] for i in range(0,x.size(0)-window_size+1,stride)]

EDIT:

A more readable solution:

# if keep_short_tails is set to True, the slices shorter than window_size at the end of the result will be kept 
def window_split(x, window_size=100, stride=50, keep_short_tails=True):
  length = x.size(0)
  splits = []

  if keep_short_tails:
    for slice_start in range(0, length, stride):
      slice_end = min(length, slice_start + window_size)
      splits.append(x[slice_start:slice_end])
  else:
    for slice_start in range(0, length - window_size + 1, stride):
      slice_end = slice_start + window_size
      splits.append(x[slice_start:slice_end])

  return splits

Answer 3

You can use Pytorch's unfold API. Refer this https://pytorch.org/docs/stable/generated/torch.Tensor.unfold.html

Example:

x = torch.arange(1., 20)
x.unfold(0,4,2)

tensor([[ 1.,  2.,  3.,  4.],  
        [ 3.,  4.,  5.,  6.],  
        [ 5.,  6.,  7.,  8.],  
        [ 7.,  8.,  9., 10.],  
        [ 9., 10., 11., 12.],  
        [11., 12., 13., 14.],  
        [13., 14., 15., 16.],  
        [15., 16., 17., 18.]])